We present here the overall framework of our method. The overall diagram is shown in Figure REF . We opt for a two stream network similar to [1]}, [2]}, [3]}. One stream takes the input RGB image \(I \in R^{3 \times H \times W}\)  as input. We use SRM filters [4]} to the RGB image and use that as an input for the other stream. SRM filters are high pass filters that enhance the high-frequency information of input image, thus highlighting the edge information more, which is helpful for localizing forgeries. We use ResNet [5]} as the backbone for both the streams. We then fuse features from both streams by concatenating features channel-wise. ASPP module [6]} is used on the fused feature map so that multi-scale information can be extracted. It is reported in [1]} that global context helps to collect more clues, such as contrast difference, etc., for manipulation detection. ASPP module helps in this regard by extracting information in different scales, such that global context as well as more fine-grained pixel level context information becomes available.
